Comments (2)
Based on my understanding of your question this should be perfectly able with the plugin.
I did a quick test with the hello-ltr repository (specifically the notebook notebooks/elasticsearch/tmdb/netfix movies.ipynb
) and created a featureset with the following code
config = {
"featureset": {
"features": [
{ #1
"name": "my_feature",
"params": ["keywords"],
"template": {
"bool": {
"must": [
{
"query_string": {
"query": "{{keywords}}",
"fields": [
"cast^9",
"title^7",
"tagline",
"overview"
],
"lenient": True,
"analyze_wildcard": True,
"fuzzy_prefix_length": 2
}
}
],
"filter": [
{
"terms": {
"title": [
"star"
]
}
}
]
}
}
}
]
}
}
client.create_featureset(index='tmdb', name='title', ftr_config=config)
The fields I am using are the ones I had in the example index. You would need to replace the fields with your fields. After you have your featureset created you can log the feature scores in the usual way. For examples you can either look into the docs or the above mentioned Jupyter notebook.
Here's an example for one query:
POST tmdb/_search
{
"query": {
"bool": {
"filter": [
{
"terms": {
"_id": [
"11",
"85783",
"7555",
"1370",
"1369"
]
}
},
{
"sltr": {
"_name": "logged_featureset",
"featureset": "title",
"params": {
"keywords": "star"
}
}
}
]
}
},
"ext": {
"ltr_log": {
"log_specs": {
"name": "log_entry1",
"named_query": "logged_featureset"
}
}
}
}
In the response you then get the logged features.
Let me know how that helps or I misunderstood your question.
from elasticsearch-learning-to-rank.
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from elasticsearch-learning-to-rank.